Federated learning is a powerful technique that enables collaborative
learning among different clients. Prototype-based federated learning is a
specific approach that improves the performance of local models under non-IID
(non-Independently and Identically Distributed) settings by integrating class
prototypes. However, prototype-based federated learning faces several
challenges, such as prototype redundancy and prototype failure, which limit its
accuracy. It is also susceptible to poisoning attacks and server malfunctions,
which can degrade the prototype quality. To address these issues, we propose
FedRFQ, a prototype-based federated learning approach that aims to reduce
redundancy, minimize failures, and improve \underline{q}uality. FedRFQ
leverages a SoftPool mechanism, which effectively mitigates prototype
redundancy and prototype failure on non-IID data. Furthermore, we introduce the
BFT-detect, a BFT (Byzantine Fault Tolerance) detectable aggregation algorithm,
to ensure the security of FedRFQ against poisoning attacks and server
malfunctions. Finally, we conduct experiments on three different datasets,
namely MNIST, FEMNIST, and CIFAR-10, and the results demonstrate that FedRFQ
outperforms existing baselines in terms of accuracy when handling non-IID data.

FedRFQ 是一种基于样本原型的联邦学习方法，旨在减少冗余、最小化失效，并提高质量。通过使用 SoftPool 机制有效地减少非独立同分布数据中的原型冗余和失效。此外，还引入了 BFT-detect 算法以确保 FedRFQ 对抗攻击和服务器故障的安全性。实验结果表明，当处理非独立同分布数据时，FedRFQ 在准确性方面优于现有基准。